Debiased inverse-variance weighted estimator in two-sample summary-data Mendelian randomization
该研究针对孟德尔随机化中弱工具变量问题，提出一种去偏逆方差加权估计量，无需筛选工具变量即可稳健估计因果效应，并讨论了处理水平多效性的扩展。
Mendelian randomization (MR) has become a popular approach to study the effect of a modifiable exposure on an outcome by using genetic variants as instrumental variables. A challenge in MR is that each genetic variant explains a relatively small proportion of variance in the exposure and there are many such variants, a setting known as many weak instruments. To this end, we provide a theoretical characterization of the statistical properties of two popular estimators in MR: the inverse-variance weighted (IVW) estimator and the IVW estimator with screened instruments using an independent selection dataset, under many weak instruments. We then propose a debiased IVW estimator, a simple modification of the IVW estimator, that is robust to many weak instruments and does not require screening. Additionally, we present two instrument selection methods to improve the efficiency of the new estimator when a selection dataset is available. An extension of the debiased IVW estimator to handle balanced horizontal pleiotropy is also discussed. We conclude by demonstrating our results in simulated and real datasets.